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Databricks Databricks-Certified-Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Pipeline Development and Orchestration	10-15%	- Databricks workflows 1. Monitoring and alerting 2. Task dependencies and orchestration 3. Jobs and job scheduling
Delta Lake	20-25%	- Delta Lake fundamentals 1. Time travel and data versioning 2. Optimize and Z-order 3. ACID transactions - Delta Lake operations 1. Delta Live Tables 2. Schema evolution and enforcement 3. Merge, update, delete operations
Data Warehouse and Lakehouse Architecture	15-20%	- Lakehouse architecture principles 1. Bronze, silver, gold data layers 2. Differences between data lake, data warehouse, and lakehouse 3. Data governance fundamentals
Data Processing with Spark	25-30%	- Spark DataFrames and Spark SQL 1. Spark SQL queries and functions 2. DataFrame operations and transformations 3. Window functions - Python and SQL for data engineering 1. Spark APIs in Python 2. Performance optimization techniques 3. Built-in and user-defined functions
Data Ingestion	15-20%	- Batch ingestion methods 1. DBR autoloader 2. Spark APIs for ingestion 3. Integration with external systems - Streaming ingestion 1. Structured streaming fundamentals 2. Kafka integration

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There is no doubt that we all dream of working for top companies around the globe. Some people make it through but some keep on thinking about how to break that glass. If you are among those who belong to the latter category, you should start the preparations for the Databricks Certified Professional Data Engineer Exam (Databricks-Certified-Professional-Data-Engineer) certification exam to improve your knowledge, expertise and crack even the toughest interview easily.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q67-Q72):

NEW QUESTION # 67
How VACCUM and OPTIMIZE commands can be used to manage the DELTA lake?

A. OPTIMIZE command can be used to compact small parquet files, and the VAC-CUM command can be used to delete parquet files that are marked for deletion/unused.
(Correct)
B. VACCUM command can be used to delete empty/blank parquet files in a delta table, OPTIMIZE command can be used to cache frequently delta tables for better perfor-mance.
C. VACCUM command can be used to compress the parquet files to reduce the size of the table, OPTIMIZE command can be used to cache frequently delta tables for better performance.
D. VACCUM command can be used to compact small parquet files, and the OP-TIMZE command can be used to delete parquet files that are marked for dele-tion/unused.
E. VACCUM command can be used to delete empty/blank parquet files in a delta table. OPTIMIZE command can be used to update stale statistics on a delta table.

Answer: A

Explanation:
Explanation
VACCUM:
You can remove files no longer referenced by a Delta table and are older than the retention thresh-old by running the vacuum command on the table. vacuum is not triggered automatically. The de-fault retention threshold for the files is 7 days. To change this behavior, see Configure data reten-tion for time travel.
OPTIMIZE:
Using OPTIMIZE you can compact data files on Delta Lake, this can improve the speed of read queries on the table. Too many small files can significantly degrade the performance of the query.

NEW QUESTION # 68
A Structured Streaming job deployed to production has been resulting in higher than expected cloud storage costs. At present, during normal execution, each micro-batch of data is processed in less than 3 seconds; at least 12 times per minute, a micro-batch is processed that contains 0 records. The streaming write was configured using the default trigger settings. The production job is currently scheduled alongside many other Databricks jobs in a workspace with instance pools provisioned to reduce start-up time for jobs with batch execution. Holding all other variables constant and assuming records need to be processed in less than 10 minutes, which adjustment will meet the requirement?

A. Set the trigger interval to 10 minutes; each batch calls APIs in the source storage account, so decreasing trigger frequency to the maximum allowable threshold should minimize this cost.
B. Use the trigger once option and configure a Databricks job to execute the query every 10 minutes; this approach minimizes costs for both compute and storage.
C. Set the trigger interval to 3 seconds; the default trigger interval is consuming too many records per batch, resulting in spill to disk that can increase volume costs.
D. Set the trigger interval to 500 milliseconds; setting a small but non-zero trigger interval ensures that the source is not queried too frequently.

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
* Exact extract: "If no trigger is specified, the default processing-time trigger runs micro-batches as fast as possible."
* Exact extract: "Trigger once processes all available data once and then stops." References: Structured Streaming triggers; Databricks Jobs and job clusters.

NEW QUESTION # 69
Which of the following section in the UI can be used to manage permissions and grants to tables?

A. Workspace admin settings
B. Data Explorer
C. Admin UI
D. User access control lists
E. User Settings

Answer: B

Explanation:
Explanation
The answer is Data Explorer

NEW QUESTION # 70
You have written a notebook to generate a summary data set for reporting, Notebook was scheduled using the job cluster, but you realized it takes an average of 8 minutes to start the cluster, what feature can be used to start the cluster in a timely fashion?

A. Disable auto termination so the cluster is always running
B. Pin the cluster in the cluster UI page so it is always available to the jobs
C. Setup an additional job to run ahead of the actual job so the cluster is running second job starts
D. Use Databricks Premium edition instead of Databricks standard edition
E. Use the Databricks cluster pools feature to reduce the startup time

Answer: E

Explanation:
Explanation
Cluster pools allow us to reserve VM's ahead of time, when a new job cluster is created VM are grabbed from the pool. Note: when the VM's are waiting to be used by the cluster only cost incurred is Azure. Databricks run time cost is only billed once VM is allocated to a cluster.
Here is a demo of how to setup and follow some best practices,
https://www.youtube.com/watch?v=FVtITxOabxg&ab_channel=DatabricksAcademy

NEW QUESTION # 71
You have accidentally deleted records from a table called transactions, what is the easiest way to restore the records deleted or the previous state of the table? Prior to deleting the version of the table is 3 and after delete the version of the table is 4.

A. 1.INSERT INTO OVERWRITE transactions
2.SELECT * FROM transactions VERSION AS OF 4
3.INTERSECT
4.SELECT * FROM transactions
B. RESTORE TABLE transactions TO VERSION as of 3
C .
1.INSERT INTO OVERWRITE transactions
2.SELECT * FROM transactions VERSION AS OF 3
3.MINUS
4.SELECT * FROM transactions
C. RESTORE TABLE transactions FROM VERSION as of 4
D. COPY OVERWRITE transactions from VERSION as of 3

Answer: B

Explanation:
Explanation
RESTORE (Databricks SQL) | Databricks on AWS
1.RESTORE [TABLE] table_name [TO] time_travel_version
Time travel supports using timestamp or version number
1.time_travel_version
2. { TIMESTAMP AS OF timestamp_expression |
3. VERSION AS OF version }
*timestamp_expression can be any one of:
*'2018-10-18T22:15:12.013Z', that is, a string that can be cast to a timestamp
*cast('2018-10-18 13:36:32 CEST' as timestamp)
*'2018-10-18', that is, a date string
*current_timestamp() - interval 12 hours
*date_sub(current_date(), 1)
*Any other expression that is or can be cast to a timestamp

NEW QUESTION # 72
......

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